A Mega-heuristic Approach to the Problem of Component Identification in Automated Knowledge Generation
The ever-narrowing bottleneck in the knowledge acquisition process begs a solution. The ongoing Automated Knowledge Generation (AKG) research at the University of Central Florida is attempting to address this issue by developing techniques for the construction of a fully functional knowledge base given a CAD representation of a process control system. A major portion of this effort is the correct identification of components given relatively unconstrained descriptive information. The Parser subsystem of AKG, detailed here, interacts with the Component Knowledge Base to fulfill this purpose by utilizing a mega-heuristic approach coupled with a search mechanism guided by fixed and dynamic levels of inductive bias.
Gonzalez, Avelino J.
Master of Science (M.S.)
College of Engineering
Length of Campus-only Access
Masters Thesis (Open Access)
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic
Kladke, Robin Rouch, "A Mega-heuristic Approach to the Problem of Component Identification in Automated Knowledge Generation" (1989). Retrospective Theses and Dissertations. 4168.